A Genetic Search In Policy Space For Solving Markov Decision Processes
Danny Barash · 1999
Markov Decision Processes (MDPs) have been studied extensively in the context of decision mak-ing under uncertainty. This paper presents a new methodology for solving MDPs, based on genetic algorithms. In particular, the importance of dis-counting in the new framework is dealt with and applied to a model problem. Comparison with the policy iteration algorithm from dynamic program-ming reveals the advantages and disadvantages of the proposed method.